Pith. sign in

REVIEW 4 major objections 5 minor 49 references

Design, Implementation, and Analysis of Fair Faucets for Blockchain Ecosystems

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that the conventional max-min fairness algorithm, run as one smart-contract function, exhausts an 8,000,000-gas block at around 10 users, while six redesigned algorithms keep per-user gas costs low, four of them scaling…

desk verdict A credible engineering study of decentralized max-min fairness faucets, with reproducible gas measurements, but the weighted schemes' missing precision parameter makes the headline results narrower than claimed. read the letter →

arxiv 2506.17236 v1 pith:JJU3SJWN submitted 2025-06-04 cs.CR cs.CEcs.DC

Serdar Metin This is my paper
classification cs.CRcs.CEcs.DC
keywords blockchainfaucetmax-minfairnesssmartcontractgascostblocklimitdenialofserviceresistanceweightedscalability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The dissertation sets out to replace the trivial faucet—a fixed-amount token dispenser on non-commercial blockchains—with a fair one. It argues that the standard max-min fairness algorithm cannot simply be compiled into a smart contract: as one centralized function it exhausts an 8,000,000-gas block at roughly ten users. The paper contributes six algorithms that split the same computation across user-submitted demand and claim calls, keeping per-transaction gas costs effectively flat as the user base grows. AMF, W/AMF, QMF, and WQMF scale to unlimited user counts, while SMF and WSMF support about 250 users, all staying within the block gas limit and preserving max-min or weighted max-min fairness. If these results hold, non-commercial blockchain networks could run faucets that are both fair and cheap, instead of vulnerable fixed-amount dispensers.

What carries the argument

The load-bearing mechanism is the replacement of a centralized distribute loop by user-driven demand and claim functions synchronized to block-number epochs. In AMF and W/AMF, each claim writes the same $\min(s \cdot w_u, d_u)$ as the textbook algorithm, with $s$ recomputed at round boundaries; weights are stored as integer approximations $\lfloor p/d_u^t \rfloor$ because the virtual machine has no floating point. QMF and WQMF replace the heap with a counting array over the bounded demand interval and accept the largest share $p$ satisfying the capacity-feasibility inequality. SMF and WSMF keep a memory-resident min-heap, run the textbook iterations on a local copy of the capacity, and return the accumulated share. The gas limit is the constraint that all these mechanisms are designed to satisfy, and the central cost-saving move is to avoid storage writes and long loops in any single transaction.

What would settle it

Run the weighted smart contracts with p set to 2 and demands drawn from [10,30) during an epoch with positive capacity; if no user receives anything because every computed weight is zero, the missing precision condition is exposed.

Watch

Extended reading notes

Core claim

The central finding is that the conventional way of running max-min fairness—collect all demands, then let one distributor loop through a min-heap and write each balance—hits the block gas limit at about 10 users under the tested 8,000,000-gas cap, because heap operations and storage writes consume the whole block budget. The paper's six algorithms avoid this by making the users carry the computation. AMF and W/AMF spread the iterative rounds into per-round claim transactions, recomputing the unit share as $\lfloor c / \sum w_u \rfloor$ when the round turns over. QMF and WQMF count demands by volume bucket and find the largest share that fits the available capacity. SMF and WSMF simulate the full max-min iteration in memory and declare one final share. The measured result is that the costly share-updating function grows roughly linearly but stays under the gas limit for the claimed ranges, while WSMF exceeds the limit beyond 250 users.

Load-bearing premise

The entire scheme depends on the precision parameter p being set high enough that no user's weight and no per-round share rounds down to zero; the paper does not state this lower bound, and if it is violated the faucet distributes nothing.

Editorial extensions

If this is right

  • Non-commercial blockchain networks can swap fixed-amount faucets for fair faucets while staying under the block gas limit.
  • AMF, W/AMF, QMF, and WQMF have hot paths independent of the number of users, so their gas costs do not grow with community size.
  • SMF and WSMF give small communities the exact simulated max-min distribution in one declared share, at the price of a 250-user ceiling under the tested parameters.
  • The same contracts can act as generic on-chain schedulers for any integer-valued resource, not only free tokens, and the weighted versions can encode long-term fairness policies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the paper never states the precision $p$ used in the tests, a fair deployment should expose and bound it; without $p \ge \max d_u$, weights collapse to zero and the faucet stops distributing.
  • AMF's final distribution round is first-come-first-served, so its equivalence to max-min fairness is not exact; a comparison on adversarial demand orders would quantify the deviation.
  • Gas comparisons in the paper are per function call, but from an operator's view the relevant total is the sum over all users' demand and claim transactions; total-cost ranking across algorithms remains an open question.
  • The quantized share-calculation loop is a general capacity-feasibility search and could be lifted out of the blockchain context into any bounded-budget fair scheduler.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript designs, implements, and measures six smart-contract variants of max-min fairness (MF) for blockchain faucets: AMF, WAMF, QMF, WQMF, SMF, and WSMF. It reports that the conventional centralized MF implementation (CMF) fails to scale beyond roughly 10 users under an 8,000,000 block gas limit, while the six contributed algorithms remain within the limit, with the first four supporting unlimited user counts and SMF/WSMF supporting up to 250 users. The paper claims the algorithms preserve max-min fairness (or a weighted version) and are resistant to denial of service. The evaluation is based on gas-cost measurements on a local Parity EVM with an instant-seal engine, and the source code and data are made available.

Significance. If the central claims hold, the work would provide a practical and inexpensive way to distribute tokens fairly in non-commercial blockchains, a niche where current faucets are naive and vulnerable to DoS. The paper's strengths are its direct gas measurements, the comparison of the custom heap against an existing implementation, and the empirical demonstration that a centralized MF loop is not viable on-chain. The availability of the contract source code and raw data is a genuine plus. However, the reproducibility of the weighted algorithms is undermined by the unreported precision parameter, the published pseudocode for SMF and WSMF contains algorithmic defects, and the AMF fairness guarantee is only approximate. These issues are load-bearing for the paper's main claims, so the manuscript requires substantive revision.

major comments (4)
  1. [Section 4.1.3, Algorithm 2, Tables 4.3/5.2/5.3] The inverse-demand weight w_u = floor(p/d_tu) and the share formula Share = floor(Capacity/TotalWeight) require not only p larger than the maximum demand (as stated) but also TotalWeight <= Capacity for any allocation to be non-zero. Since TotalWeight is approximately p times the sum of 1/d_u, the precision p has an upper bound as well as a lower bound; for the WAMF test parameters (Capacity=20n, demands uniform in [10,30)), choosing p=1000 yields TotalWeight far above Capacity and Share=0, stalling the system. The paper never reports p in Tables 4.3, 5.2, or 5.3, and the WSMF pseudocode in Appendix C uses an unreported Precision variable. Consequently, the measured gas costs do not demonstrate that the weighted algorithms actually distributed resources in the reported configurations.
  2. [Section 4.1 and Section 4.3.2] The claim that AMF and WAMF 'emulate' or 'preserve' max-min fairness is only approximate. Section 4.1 states that the last iteration is first-come-first-served among overdemanders rather than in demand order, so when capacity runs out in the final round the allocation can differ from MF. The only evidence offered (Section 4.3.2) is a cross-check with Python for the specific 3-round test distributions, and the text admits that the bound of 3 iterations is not proven. Because the epoch/round sizes in Section 4.2.1 are set to exactly accommodate 3 rounds, any distribution requiring more rounds is cut off by the epoch boundary, further weakening the fairness guarantee. The abstract and Section 1.3 should state this qualification.
  3. [Algorithm 4] The SMF pseudocode has a division-by-zero bug in the capacity-abundant case. In the main loop, if the initial simulatedShare satisfies all demands, the inner while loop (lines 43-46) empties the heap; execution then proceeds to line 52, which divides simulatedCapacity by heap.length = 0. The algorithm therefore does not terminate correctly for inputs where total demand is at most capacity, contradicting Section 5.1.3's claim that QMF and SMF produce output identical to MF for any input. The published pseudocode cannot serve as the basis for the gas results in Tables 5.4-5.6.
  4. [Appendix C] The WSMF pseudocode is internally inconsistent. The selector initialized at line 30 is used for the main loop condition at line 46, yet the data-population loop at lines 38-44 always inserts into heap[0]; for epochs with selector=1 the main loop is skipped entirely. Also, line 48 accumulates result with simulatedShare before simulatedShare is computed at line 50, and simulatedUnitShare is used at line 50 without being consistently updated. These defects make the WSMF algorithm impossible to reproduce from the specification, so the reported WSMF gas costs in Section 5.4.1 are not verifiable from the manuscript.
minor comments (5)
  1. [General] The manuscript contains numerous spelling errors (e.g., 'developped', 'inconventional', 'resouces', 'vulnarable') and would benefit from a thorough proofreading.
  2. [Tables 5.4-5.6] The dagger marks (†, ††) used in Tables 5.4, 5.5, and 5.6 are not defined in the tables; the text clarifies some of them, but the tables alone are ambiguous about which entries exceed the gas limit and which were simply not run.
  3. [Section 4.2.1 and Section 4.3.2] The gas measurements are reported as averages over '3 sets' without standard deviations or per-set data; the Python cross-check for AMF is mentioned but no simulation code or exact comparison metric is provided.
  4. [Section 1.3 and Section 4.2] The claim of support for 'unlimited' users should be qualified: while per-transaction gas cost is independent of n, the epoch span is set to 4n blocks, so the time to complete an epoch grows linearly with n; the paper does not discuss the synchronization and latency implications for large n.
  5. [Section 5.4.1] Reference [49] is used as the basis for W/AMF tests that were not repeated here, but the paper does not summarize what parameters or results were taken from [49]; this should be made explicit for reproducibility.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the central gas-cost and scalability results are independent measurements; only a minor, non-load-bearing self-citation to prior W/AMF tests is present.

full rationale

The derivation chain is not circular. The core claims are measured gas costs of Solidity smart contracts under an 8,000,000 block-gas limit (Tables 4.4-4.6, 5.4-5.6), along with algorithmic share formulas that are computed from capacity, demands, and weights by explicit equations. The CMF 'about 10 users' limit is derived from measured per-heap-operation gas and a stated iteration assumption, not from assuming the conclusion. AMF, QMF, SMF, and their weighted versions are independent constructions whose behavior is evaluated empirically, and no output is statistically forced or renamed from an input. The only self-citation is [49], used to fill W/AMF test values: 'the presented tests in the previous study [49] were considered sufficient in W/AMF.' That cited work is an externally published journal article with its own measurements and does not supply the present paper's derivation, so it is not load-bearing circularity. A separate reproducibility concern exists but is not circularity: the precision parameter p in w_u = floor(p/d_tu) is never reported, and with Algorithm 2's Share = floor(Capacity/TotalWeight) a p satisfying only the stated lower bound can make the share zero and stall distribution. This is an empirical parameter-reporting gap, not a self-referential reduction.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The only invented entity is the weighting scheme based on inverse cumulative demand, which is a policy choice rather than a postulated physical entity. The central claims rest on the water-filling equivalence axiom and on the undisclosed precision parameter, which together determine whether the algorithms actually allocate tokens.

free parameters (3)
  • Precision parameter p
    Used to compute inverse-demand weights as floor(p/d). Its value is defined symbolically in Section 4.1.3 but never stated in the test parameter tables. The required condition for a nonzero share is p approximately less than (EpochCapacity/n) times the average demand, otherwise Share becomes 0 and distribution stalls.
  • Epoch capacity C = 20n (AMF, SMF), 500Q (QMF)
    Policy parameter set in each test. It affects the measured gas costs and the number of rounds, but the algorithms treat it as an input. Not a hidden parameter, but it is a free choice that shapes the empirical results.
  • Number of AMF claim rounds = 3
    The paper assumes the distribution completes in 3 rounds based on observations, and admits there is no theoretical bound. This affects the reported per-epoch gas totals for AMF and W/AMF.
assumptions (3)
  • domain assumption QMF and SMF produce output identical to conventional MF for any given input.
    Stated in Section 5.1 without formal proof. It is a plausible water-filling property but is asserted rather than derived, and it underpins the fairness claims for these algorithms.
  • domain assumption The EVM gas cost structure is stable enough for the reported measurements to generalize.
    The paper argues absolute gas values will become obsolete but relative costs will persist. This is an unprovable assumption about future EVM compatibility, though reasonable for a 2022 testbed.
  • domain assumption The instant-seal local test environment faithfully represents epoch and round timing on real blockchains.
    The tests run on a private Parity node with instant seal, where each transaction is placed in its own block. The authors claim generalizability to all consensus mechanisms, but this assumes users can always claim and demand within the chosen epoch and round spans.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Design, Implementation, and Analysis of Fair Faucets for Blockchain Ecosystems." pith.science (2026). https://pith.science/paper/JJU3SJWN

@misc{pith2026250617236,
  author       = {Pith},
  title        = {Pith review of: Design, Implementation, and Analysis of Fair Faucets for Blockchain Ecosystems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JJU3SJWN}},
  note         = {Machine review of arXiv:2506.17236}
}
read the original abstract

The present dissertation addresses the problem of fairly distributing shared resources in non-commercial blockchain networks. Blockchains are distributed systems that order and timestamp records of a given network of users, in a public, cryptographically secure, and consensual way. The records, which may in kind be events, transaction orders, sets of rules for structured transactions etc. are placed within well-defined datastructures called blocks, and they are linked to each other by the virtue of cryptographic pointers, in a total ordering which represents their temporal relations of succession. The ability to operate on the blockchain, and/or to contribute a record to the content of a block are shared resources of the blockchain systems. In commercial networks, these resources are exchanged in return for fiat money, and consequently, fairness is not a relevant problem in terms of computer engineering. In non-commercial networks, however, monetary solutions are not available, by definition. The present non-commercial blockchain networks employ trivial distribution mechanisms called faucets, which offer fixed amounts of free tokens (called cryptocurrencies) specific to the given network. This mechanism, although simple and efficient, is prone to denial of service (DoS) attacks and cannot address the fairness problem. In the present dissertation, the faucet mechanism is adapted for fair distribution, in line with Max-min Fairness scheme. In total, we contributed 6 distinct Max-min Fair algorithms as efficient blockchain faucets. The algorithms we contribute are resistant to DoS attacks, low-cost in terms of blockchain computation economics, and they also allow for different user weighting policies.

Figures

Figures reproduced from arXiv: 2506.17236 by the authors.

Figure 1.1
Figure 1.1. Blockchain Diagram A blockchain is operated on by means of a blockchain system, which is a piece of soft￾ware, a virtual machine developed and maintained collectively by a community of users. A blockchain ecosystem, in turn, is a community of users running the same blockchain system on their local host and synchronising with each other over a p2p network. A host with a blockchain system as a p2p client on is called … view at source ↗
Figure 1.2
Figure 1.2. The operation of Max-min Fairness Algorithm [PITH_FULL_IMAGE:figures/full_fig_p017_1_2.png] view at source ↗
Figure 4.1
Figure 4.1. Epochal Layout of Matching Demands and Claims. [PITH_FULL_IMAGE:figures/full_fig_p040_4_1.png] view at source ↗
Figures from the paper (1 more)
Figure 5.1
Figure 5.1. Figure 5.1: SMF Operation Diagram that, the unit share is multiplied with the user weight within the claim function. Other than these differences, the operation of the two algorithms are identical. 5.1.5. Weighting Policy In the present dissertation, we implemented and experimen…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

49 extracted references · 49 canonical work pages

  1. [49]

    Max–min fairness based faucet design for blockchains

    Metin, S. and C. ¨Ozturan, “Max–min fairness based faucet design for blockchains”, Future Generation Computer Systems, V ol. 131, pp. 18–27, 2022. 77 APPENDIX A: AMF Pseudocode 1: procedure UPDATE STATE(Of f set, BlockN umber, Epoch, EpochSpan, RoundSpan) 2: selector ← Epoch mod (2); 3: if Epoch < j BlockN umber−Of f set EpochSpan k then 4: Epoch ← j Bloc...

    work page 2022

  2. [1]

    Bitcoin: A peer-to-peer electronic cash system

    Nakamoto, S. et al., “Bitcoin: A peer-to-peer electronic cash system”, , 2008

    work page 2008

  3. [2]

    Ethereum: A secure decentralised generalised transaction ledger

    Wood, G. et al. , “Ethereum: A secure decentralised generalised transaction ledger”, Ethereum project yellow paper, V ol. 151, No. 2014, pp. 1–32, 2014

    work page 2014

  4. [3]

    Kevm: A complete formal semantics of the ethereum virtual machine

    Hildenbrandt, E., M. Saxena, N. Rodrigues, X. Zhu, P. Daian, D. Guth, B. Moore, D. Park, Y . Zhang, A. Stefanescu et al., “Kevm: A complete formal semantics of the ethereum virtual machine”, 2018 IEEE 31st Computer Security Foundations Symposium (CSF), pp. 204–217, IEEE, 2018

    work page 2018

  5. [4]

    blockchainFaucet

    Metin, S., “blockchainFaucet”, , Oct. 2020, https://github.com/serdarmetin/blockchainFaucet

    work page 2020

  6. [5]

    Bloxberg Whitepaper, The Trusted Research Infrastructure, v1.1

    Friederike Kleinfercher, J. L., Sandra Vengadasalam, “Bloxberg Whitepaper, The Trusted Research Infrastructure, v1.1”, https://bloxberg.org/wp-content/uploads/2020/02/bloxberg whitepaper 1.1.pdf, 2020, [Online; accessed 1-March-2020]

    work page 2020

  7. [6]

    b-money, 1998

    Dai, W., “b-money, 1998”, URL http://www. weidai. com/bmoney. txt.(Last access: 08.04. 2019), 1998

    work page 1998

  8. [7]

    Hashcash-a denial of service counter-measure

    Back, A. et al., “Hashcash-a denial of service counter-measure”, , 2002

    work page 2002

Show all 49 references
  1. [8]

    180-4 secure hash standard (shs), no

    NIST, F. P., “180-4 secure hash standard (shs), no”, August. Gaithersburg: National Institute of Standards and Technology, 2015

    2015

  2. [9]

    The bitcoin backbone protocol: Analysis and applications

    Garay, J., A. Kiayias and N. Leonardos, “The bitcoin backbone protocol: Analysis and applications”, Annual international conference on the theory and applications of cryp- tographic techniques, pp. 281–310, Springer, 2015

    2015

  3. [10]

    Bitcoin’s growing energy problem

    De Vries, A., “Bitcoin’s growing energy problem”, Joule, V ol. 2, No. 5, pp. 801–805, 2018. 72

    2018

  4. [11]

    Operation Dumbo Drop: To Airdrop or Not to Airdrop for Initial Coin Offering Success?

    Li, J., X. S. Wan, H. K. Cheng and X. Zhao, “Operation Dumbo Drop: To Airdrop or Not to Airdrop for Initial Coin Offering Success?”, Hsing Kenneth and Zhao, Xi, Operation Dumbo Drop: To Airdrop or Not to Airdrop for Initial Coin Offering Success, 2021

    2021

  5. [12]

    The Rise of Decentralized Cryp- tocurrency Exchanges: Evaluating the Role of Airdrops and Governance Tokens

    Froewis, M., K. Sridhar, C. Makridis and R. B ¨ohme, “The Rise of Decentralized Cryp- tocurrency Exchanges: Evaluating the Role of Airdrops and Governance Tokens”,Avail- able at SSRN, 2021

    2021

  6. [13]

    RandSolomon: optimally resilient multi-party random number generation protocol

    de Souza, L. F., S. Tucci-Piergiovanni, R. Sirdey, O. Stan, N. Quero and P. Kuznetsov, “RandSolomon: optimally resilient multi-party random number generation protocol”, arXiv preprint arXiv:2109.04911, 2021

    2021 arXiv

  7. [14]

    Snow White: Provably Secure Proofs of Stake

    Bentov, I., R. Pass and E. Shi, “Snow White: Provably Secure Proofs of Stake”, IACR Cryptol. ePrint Arch., p. 919, 2016, http://eprint.iacr.org/2016/919

    2016

  8. [15]

    Ouroboros: A provably secure proof-of-stake blockchain protocol

    Kiayias, A., A. Russell, B. David and R. Oliynykov, “Ouroboros: A provably secure proof-of-stake blockchain protocol”, Annual International Cryptology Conference, pp. 357–388, Springer, 2017

    2017

  9. [16]

    The pgp trust model

    Abdul-Rahman, A., “The pgp trust model”, EDI-Forum: the Journal of Electronic Com- merce, V ol. 10, pp. 27–31, 1997

    1997

  10. [17]

    Survey of Consensus Algorithms for Proof of Stake in Blockchain

    Ge, L., J. Wang and G. Zhang, “Survey of Consensus Algorithms for Proof of Stake in Blockchain”, Security and Communication Networks, V ol. 2022, 05 2022

    2022

  11. [18]

    Proof-of-Prestige: A Useful Work Reward System for Unverifiable Tasks

    Kr ´ol, M., A. Sonnino, M. Al-Bassam, A. Tasiopoulos and I. Psaras, “Proof-of-Prestige: A Useful Work Reward System for Unverifiable Tasks”,2019 IEEE International Con- ference on Blockchain and Cryptocurrency (ICBC), pp. 293–301, IEEE, 2019

    2019

  12. [19]

    Proof of activity: Extending bitcoin’s proof of work via proof of stake [extended abstract] y

    Bentov, I., C. Lee, A. Mizrahi and M. Rosenfeld, “Proof of activity: Extending bitcoin’s proof of work via proof of stake [extended abstract] y”, ACM SIGMETRICS Perfor- mance Evaluation Review, V ol. 42, No. 3, pp. 34–37, 2014

    2014

  13. [20]

    Proofs of Useful Work

    Ball, M., A. Rosen, M. Sabin and P. N. Vasudevan, “Proofs of Useful Work.”, IACR 73 Cryptol. ePrint Arch., V ol. 2017, p. 203, 2017

    2017

  14. [21]

    Cryptocurrency Token: An Overview

    Shirole, M., M. Darisi and S. Bhirud, “Cryptocurrency Token: An Overview”, IC-BCT 2019: Proceedings of the International Conference on Blockchain Technology , p. 133, Springer Nature, 2020

    2019

  15. [22]

    Tokens, types, and standards: identification and utiliza- tion in Ethereum

    Di Angelo, M. and G. Salzer, “Tokens, types, and standards: identification and utiliza- tion in Ethereum”, 2020 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS), pp. 1–10, IEEE, 2020

    2020

  16. [23]

    Networks of Ethereum non- fungible Tokens: a graph-based analysis of the ERC-721 ecosystem

    Casale-Brunet, S., P. Ribeca, P. Doyle and M. Mattavelli, “Networks of Ethereum non- fungible Tokens: a graph-based analysis of the ERC-721 ecosystem”, 2021 IEEE Inter- national Conference on Blockchain (Blockchain), pp. 188–195, IEEE, 2021

    2021

  17. [24]

    A., Lottery and stride scheduling: Flexible proportional-share resource management, Ph.D

    Waldspurger, C. A., Lottery and stride scheduling: Flexible proportional-share resource management, Ph.D. Thesis, Massachusetts Institute of Technology, 1995

    1995

  18. [25]

    A fair share scheduler

    Kay, J. and P. Lauder, “A fair share scheduler”, Communications of the ACM , V ol. 31, No. 1, pp. 44–55, 1988

    1988

  19. [26]

    QoS aware group-based work- load scheduling in cloud environment

    Mohanty, S., S. C. Moharana, H. Das and S. C. Satpathy, “QoS aware group-based work- load scheduling in cloud environment”,Data Engineering and Communication Technol- ogy, pp. 953–960, Springer, 2020

    2020

  20. [27]

    Max-min fairness and its applications to routing and load- balancing in communication networks: a tutorial

    Nace, D. and M. Pi ´oro, “Max-min fairness and its applications to routing and load- balancing in communication networks: a tutorial”, IEEE Communications Surveys & Tutorials, V ol. 10, No. 4, pp. 5–17, 2008

    2008

  21. [28]

    Round-robin scheduling for max-min fairness in data networks

    Hahne, E. L., “Round-robin scheduling for max-min fairness in data networks”, IEEE Journal on Selected Areas in communications, V ol. 9, No. 7, pp. 1024–1039, 1991

    1991

  22. [29]

    Fair scheduling algorithms in grids

    Doulamis, N. D., A. D. Doulamis, E. A. Varvarigos and T. A. Varvarigou, “Fair scheduling algorithms in grids”, IEEE Transactions on Parallel and Distributed Sys- tems, V ol. 18, No. 11, pp. 1630–1648, 2007. 74

    2007

  23. [30]

    Priority service and max-min fairness

    Marbach, P., “Priority service and max-min fairness”, Proceedings. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, V ol. 1, pp. 266– 275, IEEE, 2002

    2002

  24. [31]

    Dom- inant Resource Fairness: Fair Allocation of Multiple Resource Types

    Ghodsi, A., M. Zaharia, B. Hindman, A. Konwinski, S. Shenker and I. Stoica, “Dom- inant Resource Fairness: Fair Allocation of Multiple Resource Types.”, Nsdi, V ol. 11, pp. 24–24, 2011

    2011

  25. [32]

    Bertsekas, D. P., R. G. Gallager and P. Humblet, Data networks, V ol. 2, Prentice-Hall International New Jersey, 1992

    1992

  26. [33]

    Keshav, S. and S. Kesahv, An engineering approach to computer networking: ATM networks, the Internet, and the telephone network , V ol. 116, Addison-Wesley Reading, 1997

    1997

  27. [34]

    Max min fair scheduling algorithm using in grid scheduling with load balancing

    Gogulan, R., A. Kavitha and U. K. Kumar, “Max min fair scheduling algorithm using in grid scheduling with load balancing”, International Journal of Research in Computer Science, V ol. 2, No. 3, p. 41, 2012

    2012

  28. [35]

    Blocksim: a simulation framework for blockchain systems

    Alharby, M. and A. van Moorsel, “Blocksim: a simulation framework for blockchain systems”, ACM SIGMETRICS Performance Evaluation Review, V ol. 46, No. 3, pp. 135– 138, 2019

    2019

  29. [36]

    Design patterns for gas optimization in ethereum

    Marchesi, L., M. Marchesi, G. Destefanis, G. Barabino and D. Tigano, “Design patterns for gas optimization in ethereum”, 2020 IEEE International Workshop on Blockchain Oriented Software Engineering (IWBOSE), pp. 9–15, IEEE, 2020

    2020

  30. [37]

    Gasmet: Profiling gas leaks in the deployment of solidity smart contracts

    Canfora, G., A. Di Sorbo, S. Laudanna, A. Vacca and C. A. Visaggio, “Gasmet: Profiling gas leaks in the deployment of solidity smart contracts”,arXiv e-prints, pp. arXiv–2008, 2020

    2008

  31. [38]

    Dannen, C., Introducing Ethereum and Solidity, Springer, 2017

    2017

  32. [39]

    Hedera: A governing council & public hashgraph 75 network

    Baird, L., M. Harmon and P. Madsen, “Hedera: A governing council & public hashgraph 75 network”, The trust layer of the internet, whitepaper, V ol. 1, pp. 1–97, 2018

    2018

  33. [40]

    TRON: Advanced Decentralized Blockchain Platform

    Foundation, T., “TRON: Advanced Decentralized Blockchain Platform”, , 2018

    2018

  34. [41]

    Ekiden: A platform for confidentiality-preserving, trustworthy, and perfor- mant smart contracts

    Cheng, R., F. Zhang, J. Kos, W. He, N. Hynes, N. Johnson, A. Juels, A. Miller and D. Song, “Ekiden: A platform for confidentiality-preserving, trustworthy, and perfor- mant smart contracts”, 2019 IEEE European Symposium on Security and Privacy (Eu- roS&P), pp. 185–200, IEEE, 2019

    2019

  35. [42]

    Blockchain-Based Peer-to-Peer Sustainable Energy Trading in Microgrid using Smart Contracts

    Niloy, F. A., M. A. Nayeem, M. M. Rahman and M. N. U. Dowla, “Blockchain-Based Peer-to-Peer Sustainable Energy Trading in Microgrid using Smart Contracts”, 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 61–66, IEEE, 2021

    2021

  36. [43]

    Design patterns for smart contracts in the ethereum ecosys- tem

    W ¨ohrer, M. and U. Zdun, “Design patterns for smart contracts in the ethereum ecosys- tem”, 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and So- cial Computing (CPSCom) and I...

    2018

  37. [44]

    Smart contracts: security patterns in the ethereum ecosystem and solidity

    Wohrer, M. and U. Zdun, “Smart contracts: security patterns in the ethereum ecosystem and solidity”, 2018 International Workshop on Blockchain Oriented Software Engineer- ing (IWBOSE), pp. 2–8, IEEE, 2018

    2018

  38. [45]

    SmartInspect: solidity smart contract inspector

    Bragagnolo, S., H. Rocha, M. Denker and S. Ducasse, “SmartInspect: solidity smart contract inspector”, 2018 International Workshop on Blockchain Oriented Software En- gineering (IWBOSE), pp. 9–18, IEEE, 2018

    2018

  39. [46]

    parity ethereum

    Paritytech, “parity ethereum”, https://github.com/paritytech/parity-ethereum, 2019, [Online; accessed 19-November-2019]

    2019

  40. [47]

    Time, clocks, and the ordering of events in a distributed system

    Lamport, L., “Time, clocks, and the ordering of events in a distributed system”, Com- munications of the ACM, V ol. 21, No. 7, pp. 558–565, 1978. 76

    1978

  41. [48]

    Priority Queue on Ethereum: eth-heap

    Mitton, Z., “Priority Queue on Ethereum: eth-heap”, , Oct. 2018, https://github.com/zmitton/eth-heap

    2018

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.